Image edge processing method based on guided filtering and application

Abstract

The invention discloses an image edge processing method based on guided filtering and application, and relates to the technical field of digital image processing. The method comprises the following steps of: acquiring an input image and a guide image; collecting a set first rectangular window wk1 used for weak denoising and a set second rectangular window wk2 used for strong denoising; using the window wk1 for guided filtering weak denoising, and using the window wk2 for guided filtering strong denoising; obtaining a denoising intensity absolute difference according to the filtering coefficients corresponding to the two windows, and obtaining a corresponding filtering weight according to the absolute difference; obtaining the value of the edge gradient of a strong denoising result image, and then judging pixel information needing edge smoothing processing; and based on a weak denoising result image and the filtering weight, carrying out edge smoothing processing on pixels to obtain a final result image. According to the method, edge smoothing can be achieved while image noise is suppressed and edge details are kept, the denoising intensity and the edge smoothing intensity can be independently controlled, the applicability is wide, and the flexibility is high.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image edge processing method and application based on guided filtering. Background technique [0002] As the carrier of image information, image sensor has become an important information channel in today's information age, such as mobile phone cameras, surveillance cameras, etc. When collecting image data through an image sensor, due to the influence of factors such as sensor material properties, working environment, electronic components and circuit structure, various noises will inevitably be introduced, such as thermal noise caused by resistance, photon noise, and dark current noise. , photoresponse non-uniformity noise, etc. Taking the mainstream image sensor CMOS (Complementary Metal Oxide Semiconductor, Complementary Metal Oxide Semiconductor) currently on the market as an example, the image data collected by it is obviously affected by noise, and the ...

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Problems solved by technology

[0004] However, on the one hand, guided filtering, as an edge-preserving filtering technique, can achieve a denoising effect similar to mean filtering in flat areas, thereby better removing block noise caused by video compression in flat areas, but for edge parts However, the glitch noise cannot be removed, causing edge glitches. The edge enhancement of subsequent modules will amplify the glitch phenomenon, which in turn affects the edge smoothing results. See figure 1 As shown, among them, figure 1 a represents the edge of the image after guided filtering and denoising processing, with edge burrs; figure 1 b represents the edge-enhanced image edge after guided filter denoising processing, and the glitch phenomenon is strengthened

On the other hand, in the existing edge-preserving image denoising algorithms, the denoising algorithm and the edge smoothing algorithm are difficult to control independently, which limits the applicable scenarios of the edge-preserving denoising algorithm and reduces the applicability of the edge-preserving denoising algorithm. sex

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